University Of Florida

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Autoantibodies initiate inflammation and tissue injury in patients with autoimmune disease. Antibody specificity (for both foreign- and self-antigens) is determined by clonal selection, which is a function of survival and proliferation of B cells at key developmental and peripheral activation bottlenecks. Autoreactive B cell clones are primarily removed by apoptosis-dependent negative selection, and foreign antigen-specific B cells are mainly amplified by proliferation-driven positive selection. The same extracellular ligands (e.g., CD40L and BAFF) that drive positive selection of protective B cells also rescue self-specific B cells from apoptotic negative selection. A key limitation in the field is that we do not currently understand the mechanisms underlying B cell responses to selection ligands that distinguish between foreign-reactive and autoreactive B cells. We have found that the ubiquitin ligase Itch, an essential autoimmune suppressor, specifically promotes apoptosis in negatively selected B cells exposed to survival ligands, but not B cells responding to normal positive selection cues. CD40L and BAFF activate the mammalian target of rapamycin complex 1 (mTORC1) in B cells, inducing an array of metabolic changes to support proliferation and survival, ultimately dictating selection. Mitochondrial oxidative phosphorylation has recently emerged as an essential regulator of apoptosis. We found that Itch regulates a downstream branch of mTORC1-dependent mitochondrial oxidative phosphorylation through a distinct mechanism from its role in limiting upstream mTORC1 activation. This proposal will define Itch-regulated metabolic pathways in B cells that distinguish selection of foreign-antigen specific as compared to autoreactive B cell responses.

NSF Awards · FY 2024 · 2024-08

This project aims to overcome some of the fundamental challenges in the field of implantable medical devices (IMDs) by addressing the critical issue of power source size. Traditional IMDs, such as cardiac pacemakers and neural stimulators, rely on bulky batteries, making them invasive and requiring frequent replacement surgeries. This research proposes an innovative solution using advanced wireless power transfer (WPT) technologies integrated with a novel metasurface slab. The metasurface slab, made of two-dimensional metamaterials with tailored electromagnetic properties, will be attached to the transmitter coil to concentrate electromagnetic fields, thereby increasing efficiency. By enhancing the efficiency of power transmission, this project seeks to enable the development of smaller, battery-free IMDs. These devices promise to reduce surgical invasiveness, minimize postoperative complications, and improve patient outcomes. The proposed research focuses on two main tasks: designing a metasurface slab to enhance the power transfer efficiency (PTE) of wireless power systems and validating this technology in vivo using rodent models. The metasurface slab will be attached to the transmitter coil to concentrate electromagnetic fields, thereby increasing PTE. This design process will be optimized using a deep learning approach with a conditional deep convolutional generative adversarial network (cDCGAN). The second task involves powering a miniaturized IMD for sciatic nerve stimulation in rodents, demonstrating the practical application and effectiveness of the proposed WPT system. This project leverages advanced AI techniques and aims to significantly improve the feasibility of minimally invasive, miniaturized IMDs, with potential applications across various biomedical fields. The award has the potential to significantly impact multiple research areas, including the development of wireless-powered IMDs for diverse applications, advancements in RFID technology, and innovations within the wearables, Internet of Things (IoT), and Internet of Bodies (IoB) ecosystems. The educational component will integrate research findings into a new undergraduate course, host guest lectures from industry professionals, and provide hands-on research opportunities for high school and undergraduate students. Additionally, students will participate in tours of industry facilities, offering them first-hand exposure to advanced manufacturing processes and hardware design. These efforts will prepare students for careers in biomedical engineering and foster a deep understanding of innovative IMD technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

A strong foundation in computer science (CS) will define success in the workforce of tomorrow. A key step towards increasing meaningful participation and learning in CS education for all learners is equipping their teachers with effective instructional strategies. This project will develop and study approaches to prepare 4th and 5th grade general and special education teachers to teach CS to a broad range of learners, including those with disabilities, through professional development. This project will investigate the impact of this professional development on teachers' instructional practices, as well as the learning, ability beliefs, and CS attitudes of elementary students with and without disabilities. This project includes cycles of learning and teaching with two partner school districts in Arizona. During these cycles, teacher teams will first learn about effective CS instructional practices and then implement these practices within classrooms. Teachers will engage in collaborative planning, lesson feedback cycles, and technical support during teaching. The study is guided by both development and impact research questions. Development questions include: How do 4th/5th grade and special and general education teachers adapt this professional development to their instructional practices to increase the participation of all learners, including students with disabilities in CS education? Impact questions include: How does participation impact teachers' competence in teaching CS to all elementary students, including those with disabilities? This project will contribute to the empirical literature on effective online, sustained professional learning to elementary and special education teachers and expand opportunities for all students in CS education. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.  This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

PROJECT ABSTRACT/SUMMARY Growing evidence suggests the presence of dysregulated pain modulation in older adults, and affect which may heighten age-associated risk for chronic pain. Additionally, chronic pain and Alzheimer’s Disease and related dementias (AD/ADRD) are highly prevalent and comorbid in older adults, and research suggests that they may have overlapping etiologies and pathologies. Chronic pain may a predictor for the development of AD, and almost half of AD patients report having pain. Thus, understanding of the shared mechanisms underlying both is critical in order to develop effective treatment and prevention modalities. Recently, epigenetics has been implicated in both disease states, with many modifications of the epigenome that may go on to result in immune system dysfunction, of which is a hallmark of both chronic pain and AD. While there are many environmental factors that can influence the epigenome, nutrition status has been shown to be one of the most common and modifiable factors therein. Thus, it may be efficacious to understand dietary interactions with the epigenome to target epigenetic regulation of the development and maintenance of chronic pain and AD. Therefore, the overall goal for this mentored career development proposal (K99/R00) is to fill this knowledge gap and determine the influence of overall diet pattern as well as Vitamins A and D specifically on the epigenetic environment as it relates to chronic pain and AD/ADRD. Primary training goals for the current proposal are to: Increase knowledge and understanding of measurement techniques used to assess cognitive aging in humans, with a specific focus on mild cognitive impairment, and AD/ADRD; Further expand knowledge of nutri-epigenetics, and apply it to cognitive aging outcomes; Enhance clinical research skills related to the design, conduct and statistical analysis of multidisciplinary studies and rigorous translational research skills to function as an independent investigator. Study 1 (K99 Phase) will assess dietary differences and their associations with differences in epigenetic aging, pain, and cognition in individuals with and without chronic pain. Study 2 (R00 phase) will allow for the assessment of diet pattern as well as vitamin A and D status on DNA methylation patterns, gene and protein expression, pain and cognitive outcomes in older adults with and without mild cognitive impairment. This proposed career development plan extends from the PIs prior work in dietary and immune system modulation of pain, and will forge a path towards understanding and investigating side-effect free nutrigenomic targets that improve pain and AD/ADRD outcomes in older adults.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/Abstract Dental care-related fear, anxiety, and/or phobia (hereafter, DFA) traditionally refers to the emotional, behavioral, and/or physical responses that may occur when thinking about or engaging in dental care. There is ample evidence in the scientific literature that DFA is associated with greater prevalence of oral conditions and diseases, including dental caries, tooth loss, and periodontal disease, leads to avoidance of both preventive and restorative dental care, and impairs oral health-related quality of life. Moreover, these observed associations appear to pervade populations and are global in nature, and, although significant advances in pain management and anxiety control, society-wide estimations of the prevalence of dental care-related fear/anxiety have remained relatively constant for over half a century. Despite the impact of DFA on oral health, the scientific literature is replete with terminology that refers only to dental fear or only to dental anxiety, yet in the broader psychological literature, fear and anxiety are known to be separate constructs with unique manifestations. This lack of consistency and interoperability between the medical and dental care communities in defining and classifying such phenomena has contributed to the current stalemate in scientific progress as it relates to understanding the etiology and implications of such phenomena in the dental care context. Without a consensus on the definition, types, scope, and etiology of DFA, the associated individual, clinical, and population impacts, and viable strategies for intervening to mitigate or manage such impacts the impact of DFA cannot be adequately studied. To address these terminological shortcomings, we have created the Ontology of Dental care-related Fear, and Anxiety, and/or Phobia (ODFA). By more precisely representing the types of fear and/or anxiety experienced by individuals, the ODFA’s concepts and relations facilitate the development of tools and resources capable of enhancing our understanding of the individual, clinical, and population impacts of dental care-related fear and anxiety. When applied to study data, the ODFA enables the integration and interoperability of data from multiple studies, which, in turn, provides a means to perform rigorous analysis on larger datasets in order to gain an in-depth understanding of the phenomenon and treatments for DFA.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT People living with dementia (PLWD) from racial-ethnic minoritized groups and socioeconomically disadvantaged environments are more likely to face barriers to diagnosis, care, and services. Multiple social determinants of health (SDoH) contribute to the disparities in Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) progression and the quality of AD/ADRD care. Thus, AD/ADRD is a public health crisis that must be managed not only by traditional medical care but also by addressing patients’ unmet social needs. Artificial intelligence (AI) and large real-world data (RWD), such as electronic health records (EHR), offer an opportunity to develop innovative approaches that improve health and health equity by addressing SDoH. The objective of this project is to develop a machine learning (ML)-based social risk management platform - ISMART (intelligent Social risk Management in AD/ADRD paTients) - that can be embedded into EHR systems to improve the quality of care and quality of life of PLWD. We will use RWD from the OneFlorida+ network, a member of the National Patient-Centered Clinical Research Network (PCORnet), comprising EHR data from >20M individuals. We will leverage our prior work that established an external exposome database with contextual SDoH measures documenting social and physical environments and a natural language processing pipeline that can extract person-level SDoH (including caregiver information) from clinical narratives in EHRs. Our study will follow an intervention mapping approach that engages a Stakeholder Advisory Committee to achieve three Specific Aims. In Aim 1, we will build an RWD cohort of PLWD and to identify key contextual and person-level SDoH associated with PLWD care and outcomes. In Aim 2, we will develop ML- based social risk management algorithms for dementia care and outcomes, including (a) a fair individualized polysocial risk score (iPsRS) to screen for unmet social needs in PLWD; and (b) causal-principled AI methods to quantify the causal, heterogeneous effect of key actionable SDoH (e.g., food) on PLWD care and outcomes. In Aim 3, we will co-design with stakeholders the ISMART platform, including (a) prototyping ISMART platform following a User-Centered Design process; and (b) developing recommendations for future implementation and evaluation via focus groups and Delphi panels. The success of our project will lead to the development of ISMART prototype for social risk management in PLWD, with a set of strategies for future implementation and evaluation. Our innovative, structured approach to integrating social risk management with health care of PLWD may lead to a necessary paradigm shift in US health care delivery.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT Glucose is the most important source of energy for proper brain development and functioning; therefore, any imbalance in glucose is associated with changes in neurodevelopment. Hypo- and hyperglycemia impact brain structure and functioning and cognitive abilities across the lifespan and it is well-established that older adults with type 1 diabetes (T1D) experience cognitive impairment. Indeed, individuals with T1D have a 65% increased risk for dementia later in life compared to 37% in those with type 2 diabetes. Increasing glucose metabolism during childhood is related to growth of healthy brain structures and connections; but for individuals with early childhood onset of T1D, decreases in brain structures and functions and lower neurocognitive abilities and IQ occur. Brain imaging studies of individuals with T1D demonstrate reductions in total brain volume and gray and white matter density in the cerebellum; and changes in the frontal, parietal, and temporal lobes; hippocampus; thalamus; and various structures in the occipital lobe. Individuals with T1D generally demonstrate challenges with mental flexibility, intelligence, memory, executive functioning, and processing speed with earlier age of onset, longer T1D duration, suboptimal glycemia, hypo- and hyperglycemia and extreme glycemic variability, and diabetic ketoacidosis serving as risk factors for worse cognitive performance, lower IQ, and brain structural changes. Studies focused on neuroimaging and neurocognition in the context of T1D biomarkers, social determinants of health, sleep and psychological, social, and school functioning with children with new-onset T1D in the United States are extremely sparse. Therefore, there is an urgent need to address these gaps in the extant literature to identify opportunities to prevent or mitigate cognitive challenges across the lifespan. The overarching goals of the Diabetes Brain and Cognitive Development – Type 1 Diabetes (DBCD-T1) study are to 1) understand the impact of T1D on brain and neurocognitive and psychosocial functioning; 2) examine risk and protective factors associated with T1D- related neurocognitive impact; and 3) assess associations of diabetes technologies and neurocognitive functioning in children with T1D. We will recruit n=160 prepubescent children with new-onset T1D and their caregivers from the Northwest, North Central, and Northeast districts of Florida, with sociodemographic characteristics representative of the US population of children with T1D. Study tasks include brain imaging and comprehensive neurocognitive and academic testing (e.g., IQ, memory, executive functioning, processing speed, reading, mathematics, writing); questionnaire completion (e.g., anxiety, depression, quality of life, resilience, fear of hypoglycemia, T1D family conflict, diabetes distress); and collection of biological (e.g., autoantibodies, C-peptide), sleep (actigraphy), and diabetes device and glycemic data (e.g., device downloads, A1C). Throughout the study, we will continuously partner with and consult our Community Advisory Board using principles of community engagement and science to maximize the success of our aims.

NSF Awards · FY 2024 · 2024-08

Estuaries support unique ecosystems and provide valuable economic benefits, such as aquaculture farms and recreation. They require careful management in the face of a changing climate and increasing coastal development. Estuaries with low river inflow for part of the year, found for example in California, Texas, and Latin America, are particularly vulnerable to depletion of freshwater resources and increasing drought conditions. However, predicting the response of a low-inflow estuary to these new conditions is challenging. This challenge arises in part because low-inflow estuaries behave differently than traditional estuaries which have year-round river flow. Most theory for estuary circulation focuses on traditional estuaries and does not apply well to low-inflow estuaries. Our project aims to fill this knowledge gap by testing how low-inflow estuaries respond to extreme weather and drought. The project results will generate a new understanding of the circulation of low-inflow estuaries. It will also help inform coastal managers on how low-inflow estuaries are likely to respond to future variability. The research will be paired with an educational component including school labs and field trips to promote ocean science among students. The proposed project aims to enhance knowledge of future changes to low-inflow estuaries using models. For high-inflow systems, idealized modeling studies have investigated the estuarine adjustment to changes in river runoff or tidal forcing. However, analogous studies for low-inflow estuaries are lacking, despite the global prevalence of low-inflow systems. This research will develop numerical simulations of low-inflow estuaries using the hydrodynamical model ROMS (Regional Ocean Modeling System). The primary goal of the simulations will be to evaluate the circulation response to strong surface thermodynamic forcing and periodic but intense freshwater pulses mimicking future climate variability. The experiments will also test the conditions necessary for the formation of mid-estuary density minima (thermal plugs) and maxima (salt plugs) features which can impact residence time and mixing. Both density-driven and tidally driven circulation regimes will be generated by varying factors such as estuary depth, tidal amplitude, and air temperature. Using a range of parameters will enable broad applicability of results. Passive dye experiments will also be run to quantify flushing timescales and their response to extreme weather. Therefore, in addition to providing a physical understanding of low-inflow estuaries, the results will have direct relevance to biological and chemical analysis of low-inflow estuary ecosystems. The proposed research will generate new ideas for understanding the physics of low-inflow estuaries, helping to build a contemporary framework. This framework can then form the basis of future interdisciplinary research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project conducts a first-of-its-kind study of the role that workplaces played in accelerating individual income inequality in the United States from 1996 to 2021. Analyses of the research identify the changes in employment, earnings, and fringe benefits that most contributed to the growth of economic inequality in the last thirty years. The findings uncover insights with implications for public, tax, and labor market decisions in the U.S. The research leverages a bespoke set of confidential administrative data sources with linked employer-employee records and implements a novel empirical strategy consisting of hierarchical modeling and decomposition of variance components. In doing so, the research makes four key contributions: First, it theoretically formulates and empirically examines hypotheses laying out the structural changes within organizations, as well as at the occupation and job levels, that can account for the observed trends of rising between workplace earnings inequality. Second, the research shows how structural changes in employment patterns contribute to rising between-workplace inequality. Third, the study updates and expands previous work on fringe benefits and total compensation inequality through 2021. Finally, the study provides the first analysis of how inequalities related to demographic category potentially explain patterns of rising between-workplace inequality through sorting, and how growing between-workplace inequality exacerbate incomes gaps between demographic categories. The research contributes to the literatures on organizational restructuring, skill-biased technological change, organizational inequality, and demographic inequality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational waves (GW), predicted by Albert Einstein's theory of General Relativity, are the ripples in the fabric of space-time created by relativistic astrophysical systems. In 2015 the NSF's Laser Interferometer Gravitational-wave Observatory (LIGO) made the first direct observation of gravitational waves confirming the major prediction of General Relativity and opening an unprecedented new window into the cosmos. This new observational channel probes the most unusual astrophysical objects in the Universe and promises to discover new and possibly unexpected astrophysical phenomena. In the next few years, hundreds of detections of GW sources are expected as LIGO reaches its design sensitivity and the other instruments join the worldwide network of gravitational wave detectors. Even higher detection rates are expected when the LIGO is fully upgraded to its A+ configuration. These experiments will address the key research questions including the formation and evolution of black holes and neutron stars, the role of compact objects in high energy emission, the properties of nuclear matter, the nature of gamma-ray bursts, and probe the structure and evolution of the Universe, and, possibly, physics beyond General Relativity. This award promotes science targeting observations of gravitational waves for a wide range of astrophysical systems, and the discovery of new GW sources. This award enhances the broader effort on education and training and provides unique educational and research opportunities for students and junior scientists. This award reinforces the success of the LIGO data analysis effort at the University of Florida and supports new innovative research projects that have the potential to produce transforming results. It targets the most extreme astrophysical events, such as recently discovered mergers of neutron stars and black holes and holds promise for discoveries of entirely new astrophysical objects. The GW searches supported by the award utilize detection and reconstruction algorithms, that use minimal model assumptions. They are capable of detecting GW signals in a wide range of source parameters, including yet unknown sources. Among the primary detection targets are the Intermediate Mass Black Holes (IMBH), dynamic binary black hole mergers, and core-collapse supernovae, whose observations will address many open questions in astrophysics. The project activities extend the most promising GW searches for compact binary sources into the binary parameter space not yet explored by the existing template algorithms. Searches supported by the award have the potential to study IMBH sources, constrain the pair-instability mass gap, look inside the galactic nuclei by detecting dynamic binaries, and test the theory of general relativity at the high field regime by comparing the reconstructed signal waveforms with the numerical relativity predictions. Rapid source reconstruction and sky localization promote joint observations with electromagnetic telescopes and neutrino detectors advancing the field of multi-messenger astronomy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Decentralized Federated Learning (DFL) has emerged as a new learning paradigm in artificial intelligence, enabling the training of data-hungry learning models on local devices without sharing raw data. This paradigm is immune to the single-point failure of a central server and the privacy perils caused by a dishonest server. However, the understanding of DFL is still in its infancy. It is unclear how the decentralized and periodic communication strategy affects the convergence performance of DFL algorithms, especially when tackling emerging machine learning models where the corresponding optimization problem has complicated structures, such as bilevel optimization. Furthermore, peer-to-peer communication in DFL introduces unique security risks, stemming from a combination of malicious users and device-to-device communication patterns. This project aims to design and develop a secure and efficient DFL system, addressing these communication, computation, and security issues. This project will benefit a variety of high-impact applications where machine learning models are trained in a DFL setting without sharing raw data. This project aims to develop computational theories, models, and prototype systems, forming the foundations for trustworthy DFL, considering both high-performance accuracy and security with privacy preserving. The first focus of research is to develop the structural communication topology and pattern, underpinned by mathematical graph theories and empirical computer network techniques, to favor efficient and robust communication. The second focus is to investigate the bilevel optimization problem for emerging machine learning models in DFL, where efficient stochastic bilevel optimization algorithms will be developed, and their theoretical convergence foundations in DFL will be established. To providing security guarantees, unique security threats to DFL will be thoroughly investigated and principled defense strategies will be developed accordingly. Beyond these foundational aspects, this project will apply the developed techniques to practical data mining applications in Internet-of-Things networks and Smart Transportation, addressing the unique challenges therein and providing practical solutions to benefit real-world applications. Moreover, the team will integrate the proposed research work into several courses and provide abundant research activities for both undergraduate and graduate students with diverse backgrounds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

With the efforts to re-shore semiconductor manufacturing and strengthen the U.S. semiconductor ecosystem, semiconductor data and models are playing an increasingly important role in semiconductor design, research, and education. In the materials research and education community, materials databases and machine learning models have been developed and deployed. These efforts have greatly facilitated materials research and education. In contrast, in semiconductor device technologies, such capabilities are important but significantly lag behind. Semiconductor device models, which are fundamental to circuit and system research, require significant domain knowledge of device physics to develop from scratch or to extract a large number of model parameters, which limits their accessibility. This project will develop and deploy a foundational semiconductor research and education cyberinfrastructure (CI) to address these gaps. The CI will be deployed in the form of an integrated device database and ML models. Establishing a conveniently accessible cyberinfrastructure on shared semiconductor device data and ML models will provide semiconductor researchers with a powerful tool, lower the entry barrier, and facilitate broad participation in research and education in the area of semiconductor devices and design. The objectives of this program are to develop and deploy a broadly accessible CI of integrated machine-learning device models and device data for empowering semiconductor device research and education, to understand and explore how to use advanced TCAD simulations to efficiently obtain accurate device data for database deployment, to interface the device data with machine learning methods for producing accurate and reusable ML semiconductor device models, and to self-sustain the semiconductor device CI through developing accompanying learning modules and enabling user participation. The semiconductor devices included in the CI are important to future computing, memory, power electronics, and quantum computing interface technologies. The project will develop a foundational semiconductor research and education CI tool in the form of a device database and ML models, and contribute to strengthening the semiconductor ecosystem by making semiconductor device data and ML models readily accessible. This project is jointly funded by the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program and the EDU Core Research (ECR) program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project addresses the critical need for advanced training in microelectronics physical assurance, inspection, and metrology, a field essential to the integrity and security of electronic components in the global supply chain. With the enactment of the CHIPS Act, the United States is focused on strengthening domestic microelectronics manufacturing. However, there is a significant shortage of skilled labor capable of performing physical test and assurance tasks, primarily due to the high costs and limited availability of traditional laboratory facilities as well as the complexity of the subject matter. This project leverages Virtual Reality (VR) technology to enhance accessibility and engagement for education. VR's interactive 3D graphics and immersive simulations offer an innovative solution to personalize learning experiences and engage students effectively. VR MiPA aims to incorporate gamification elements to facilitate student progress tracking. Hosted by the University of Florida’s SeCurity and AssuraNce (SCAN) lab and supported by experts in VR interaction design and digital learning environments, the VR training will be integrated into an existing course to enrich the curriculum and transform how physical assurance, inspection, and metrology education is delivered. VR MiPA will develop interactive VR modules featuring 3D graphics and simulations, the incorporation of gamification to motivate and track student learning, and the integration of this training into an existing microelectronics course at the University of Florida. The technical approach involves collaboration between experts in microelectronics physical assurance, VR interaction design, and educational technology. The project will conduct a comparative study of learning outcomes between VR-based training and traditional on-site training using quizzes and surveys to assess student performance and engagement. Additionally, the project will collect and analyze user behavior data from the VR application to evaluate its effectiveness and impact. Dissemination efforts will extend the VR training to a broader audience including professional trainees and K-12 educators through web-based platforms and industry conferences. VR MiPA is expected to make significant contributions to the fields of human-computer interaction; educational technology; and microelectronics assurance, inspection, metrology with aims to prove insights into the efficacy of VR as a training tool in STEM education and enhance workforce development in microelectronics. This award is co-funded by the NSF Improving Undergraduate STEM Education (IUSE: EDU) Program and Advancing Informal STEM Learning (AISL) Program. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. The NSF AISL Program supports research on the design, development and impact of STEM learning opportunities and experiences for the public in informal educational environments. This project is further supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The availability of large-scale mobile location data, which can track millions of people’s movements over time with high spatial resolutions, is transforming human mobility research. However, the potential spatial biases contained in such data, which reflect systematic errors or inaccuracies in geographic information, pose a significant challenge to the reliability and validity of research findings derived from them. This project tackles this challenge by advancing knowledge on the causes and extent of spatial biases associated with large-scale mobile location data and developing methods to mitigate these biases. It contributes to theoretical and methodological advances in geographical and behavioral sciences and their intersection in the context of human mobility research. The research findings advance STEM education and inform transportation agencies that use mobile location data to develop more equitable transportation plans and policies. This project supports research on quantifying, identifying the causes of, and mitigating potential spatial biases in large-scale mobile location data used for human mobility analysis. It addresses four major research tasks: 1) Developing metrics and analytical frameworks to evaluate bias, collecting multi-sourced datasets, and then quantifying spatial bias in sample representation and mobility measurements; 2) conducting mixed-methods research to identify potential causes of spatial biases from the mobile location data generation process and assessing how algorithmic uncertainties can result in spatial bias; 3) developing new methods to mitigate spatial bias in mobile location data; and 4) publishing research products, building partnerships, and promoting results adoption. The methods produced can be generalized to address spatial biases in other emerging datasets such as geo-tagged social media data and participatory Geographic Information Systems data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This doctoral dissertation project examines the impact of ecotourism on the health of four non-human primate species. The development of antimicrobial agents positively transformed the treatment and prevention of infectious diseases. This innovation, however, unintendedly triggered the evolution of antimicrobial resistance genes in pathogens. These genes are transmitted both vertically (parent-offspring) and horizontally (between unrelated organisms) which permits their transfer between species. Antimicrobial agents and resistance genes have led to an accelerated evolutionary arms race between pathogens and hosts. Thus, identifying antimicrobial resistance genes and their transmission is fundamental to our knowledge of current and future epidemiological scenarios. The study establishes geographic patterns of ecotourism-associated human activity with imaging technology. Soil samples are analyzed to determine whether ecotourists unwittingly create reservoirs of microbiome antimicrobial resistance genes in the soil. Researchers analyze non-human primate fecal samples to establish the presence and patterns of microbiome antimicrobial resistance genes in these species. Results from this study reveal the levels of antimicrobial resistance among wild non-human primates. The study provides training and learning opportunities for students and inform the effectiveness and consequences of a common conservation strategy (ecotourism). The introduction of new antimicrobial resistance genes into non-human primates’ gut microbial communities has the potential to generate microbiome unbalances and negatively impact these species health. As ecotourists enter protected areas with greater frequency and come in close contact with non-human primate species, they increase the chances that they leave behind new antimicrobial resistance that threatens these species long-term survival. This dissertation project has three specific objectives: (1) understand how ecotourists are distributed across these species' landscapes, (2) determine if ecotourists posit a threat to these species through the creation of environmental reservoirs of antimicrobial resistance, and (3) examine the role of ecotourists as vectors of antimicrobial resistance. To achieve these objectives, researchers leverage camera trap data spanning back to 2017 in combination with DNA extraction and sequencing of antimicrobial resistant genes from environmental and fecal samples. Data on ecotourist distribution gleaned from the camera traps is combined with metagenomic sequencing data from the soil and fecal samples to explore the relationship between ecotourist activity and the abundance and diversity of antimicrobial resistance genes in soil and feces. This study examines the impacts of ecotourism-mediated antimicrobial resistance transmission at a level. The study unveils how a more cryptic mechanism of anthropogenic disturbance may be impacting non-human primate populations and undermining the utility of ecotourism as a critical conservation tool. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

The mission of our Wellstone Center is to perform cutting-edge research that leads to transformative therapies for myotonic dystrophy type 1 (DM1) and 2 (DM2). Our Center is based on a long history of creating and maintaining exceptionally close research interactions between basic scientists at the University of Florida and clinical researchers at the University of Rochester. For this proposed Wellstone Center, we will transition to the University of Florida as the primary Project/Performance Site but will maintain a similar leadership structure. All 3 projects are highly synergistic and center on 2 themes: 1) continuing to accelerate and support clinical trials in the DM1 space, including preparing for a potentially imminent post-approval landscape; 2) accelerating both basic science and translational efforts for DM2 so that DM2 can also rapidly advance to clinical trials. Project 1 focuses on two DM2 mouse models that we have recently generated, including the first human BAC transgenic mouse multisystemic model for DM2 (CNBP-DM2) and a human skeletal muscle actin (HSA) transgenic model (HSA-DM2) similar to the HSALR mouse model for DM1. Preliminary studies demonstrate these models collectively reproduce characteristic features of DM2, including CCTG repeat instability, nuclear RNA foci, MBNL sequestration, RNA mis-splicing and RAN translation. The detailed molecular, histological, and physiological effects due to multisystemic expression of CNBP-DM2 and skeletal muscle expression of HSA-DM2 transgenes will be assessed and these studies will be used to test two therapeutic strategies. Project 2 focuses on clarifying molecular mechanisms underlying several leading therapeutic approaches using a new Dmpk CTG expansion knockin multisystemic model for DM1 that reproduces characteristic DM1 molecular and pathophysiological features. Project 3 transitions to current clinical issues by analyzing a large DM1 cohort using a remote assessment strategy to examine disease severity. A DNA Bank will also be established for comprehensive analysis of expanded repeats and statistical models will be developed to study relationships between repeat length, age, sex and disease severity. The hypothesis that variant repeat interruptions are associated with reductions in disease severity, somatic expansion and mis-splicing will also be tested. For DM2, remote patient assessments and targeted recruitment will facilitate studies designed to assess roles for RAN translation and RNA toxicity. In combination with these projects, our Administrative Core will provide rigorous oversight for all projects and cores and emphasize synergy between the projects and cores. We will continue to expand our Shared Resource Core since this core is an essential resource for our Wellstone Projects but also serves the broader research and clinical community focused on dominant muscular dystrophies. Our Training Core is designed to attract and build competency in the next generation of basic and clinical investigators focused on DM1 and DM2 by training in wet and dry lab settings and will provide training in communication skills needed to effectively engage all DM stakeholders.

NIH Research Projects · FY 2025 · 2024-08

Project Summary Tumor-specific immunotherapy is a promising modality capable of improving clinical outcomes for children affected with high-grade glioma (HGG). We have pioneered a ‘first-generation’ adoptive cellular therapy (ACT) platform utilizing total tumor RNA-pulsed dendritic cells (DCs) to expand polyclonal tumor-specific T cells for the treatment of invasive and refractory brain cancers. We have demonstrated the safety, feasibility, and promising pre-clinical and early clinical efficacy of this approach, including prolonged disease-free remission in some treated subjects (>2 to 5 years) and radiographic and clinical response in first-in-human phase I/II clinical trials treating patients with medulloblastoma (MB), HGG, and brain stem glioma (FDA INDs 14058 and 17298). In this proposal, we will develop a ‘next-generation’ precision ACT approach using a combination of patient-specific antigen profiling using a novel cancer immunogenomics-based algorithm developed in our lab, a novel gene enrichment strategy that allows us to target hundreds of tumor-specific antigens in a single pool of RNA (tsRNA), and the SELEX-CTL (Selective Expansion of Cytotoxic T Lymphocytes) platform for the stimulation and selective enrichment of polyclonal tumor-reactive T cells from precursor frequency of 1-3% after stimulation with tsRNA- pulsed DCs. This powerful approach to generating enriched T cell populations recognizing a plurality of tumor antigens uniquely addresses the challenge of dealing with tumor antigenic heterogeneity and confronting the reality of patient-to-patient variation in antigen expression in the development of antigen targeting strategies. We hypothesize that given the preclinical and clinical data we have generated demonstrating the capacity to engender a polyclonal T cell response against HGGs and MB antigens using DCs pulsed with unrestricted total tumor RNA, the approach studied within this proposal of selectively identifying and amplifying patient-specific tsRNA and uniquely identifying, sorting, and expanding responding tumor-specific T cells for use in ACT will constitute a highly significant and highly effective precision immunotherapy approach for the treatment of pediatric patients with invasive HGG. Our SPECIFIC AIMS will be to: 1. Evaluate the safety, efficacy, and immunologic effects of ACT targeting neoantigens and uniquely expressed tumor-associated antigens (TAAs) in preclinical models of pediatric HGG; 2. Determine the capacity to selectively isolate and expand antigen-specific memory T cells in vitro from pediatric patients with HGG who have received ACT at our center; 3. Conduct a phase 1 clinical trial of precision ACT targeting tumor-specific antigens in children, adolescents, and young adults (AYAs) with invasive HGG.

NIH Research Projects · FY 2025 · 2024-08

Project Summary A central question in the genetics of complex traits is understanding how variation in DNA sequences leads to variation in phenotype. Recent technological advances in high-throughput phenotyping assays for model organisms and the establishment of large human biobanks and consortium databases have provided opportunities to study the genotype-phenotype maps for complex traits in unprecedented detail. However, it remains a major challenge to model and interpret these data due to the intrinsic high dimensionality of the genotype space and the many ways in which causal genes can interact. My research program is focused on developing new theoretical frameworks and interpretable computational tools to analyze large genotype- phenotype datasets with the goal of (1) accurately predicting phenotypes for novel genotypes and (2) providing biological insights into the genetic architecture of complex traits by identifying key genes, gene interactions, and pathways. The primary focus for my lab in the next five years is to develop new Bayesian and machine learning methods capable of modeling the full spectrum of genetic interactions including pairwise as well as higher-order epistasis. Specifically, we are combining rigorous mathematical modeling with modern machine learning techniques to develop a suite of scalable, principled methods to achieve accurate phenotypic prediction and accelerate the discovery of novel genetic mechanisms. While proof-of-concept versions of many of the proposed methods display state-of- the-art performance, substantial work remains to scale the methods to larger genotype-phenotype datasets, test model performance on a wide range of complex traits and organisms, and interpret the results to gain biological insights. In the coming years, we plan to build these methods into an integrated framework for analyzing complex genetic interactions, which will include computational pipelines for fitting accurate phenotypic prediction models, identifying gene interactions and pathways for experimental validation, and quantification of estimation uncertainty. We will also prioritize the development of user-friendly, GPU-accelerated software packages for all methods. Important applications of the proposed research directions include predicting disease risks in humans, elucidating the genetic mechanisms for economically and clinically important traits, and designing improved plant and animal breeding programs. The computational tools developed here will be broadly useful to geneticists, evolutionary, and clinical biologists.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/Abstract: Resistance to our major antibiotics has been identified by the CDC as a major threat to the health and safety of the American public. Two of the highest threat pathogens are carbapenem-resistant Acinetobacter baumannii (CRAB) and Klebsiella pneumoniae (CRKP). Over the last decade, we have seen the emergence of novel resistance mechanisms, limiting the utility of our best antimicrobials. This proposal answers a call to arms from NIAID, who set forth the tool development program (RFA-AI-16-081 in 2017) to generate mechanistic insights that can be used to create antibiotic combinations that are rationally optimized to kill CRAB and CRKP. Further, there has been increasing awareness of organism state(s) such as tolerance/Non-Replicative Persister (NRP) phenotype that allows evading the lethal action of antimicrobial therapy. It is important to gain insights into this to design approaches to suppress organism entry into NRP state and, if already present, design regimens that can eradicate NRP. We will create novel mechanistic insights and use them to rationally optimize combination dosing strategies to synergistically kill CRAB and CRKP, and to suppress resistance. The impact of resistance mechanisms (e.g. efflux, β-lactamases, and porin channels) and of non-essential penicillin-binding protein (PBP) receptors on bacterial killing and resistance emergence will be studied. To optimally suppress resistance, we will approach this problem in 4 dimensions, and consider the changes in PBP expression over time (i.e. growth phase) and the cellular locations of these resistance mechanisms. This P01 contains 3 Projects and 3 Cores. Project #1 will use our tools from RFA-AI-16-081 to gain insights into how different PBP binding profiles affect killing and resistance suppression. This project will leverage the Mechanistic Assay Core and the Mathematical Modeling Core to design optimal, clinically feasible dosage regimens. Project #2 will examine these regimens against CRAB and CRKP isolates in the Hollow Fiber Infection Model (HFIM). In Project #3, we will study the best regimens (and lesser regimens, as controls) from the HFIM in two murine models of pneumonia (granulocyte replete and granulocytopenic). This will provide insights into how granulocytes can best enhance antimicrobial therapy. The Administrative Core will serve as the overall data repository and clearing house, and facilitate communications. The Mechanistic Assay Core will leverage transcriptomic, proteomic, flow cytometry, and resi- stance mechanism assays, closely integrated with PBP binding studies and isogenic strains from Project #1. This core will generate critical insights into the mechanisms of antibiotic action, resistance and synergy. Finally, the Mathematical Modeling Core will develop high dimensional mathematical models that will integrate all experi- mental data from the Projects and Cores to provide robust, efficacious and clinically relevant dosage regimens. We will prospectively validate these model predictions in the HFIM (Project #2) and in normal and neutropenic murine pneumonia models (Project #3) to support evaluation of these synergistic regimens in future clinical trials.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT Despite the major advances in the treatment of human immunodeficiency virus (HIV) and HIV-associated comorbidities, the adoption of newer regimens in children has fallen far behind that in adults. Consequently, health outcomes of children living with HIV (CLWH) is worse than in adults. Of the CLWH receiving antiretroviral therapy (ART) in 2022, 81% had a suppressed viral load, compared with 93% of adults aged 15 years or older. In the same year, children under 15 years old accounted for only 4% of all people living with HIV (PLWH), but they accounted for 13% of all AIDS-related deaths. For decades, children were forced to use adult formulations or poorly tolerated pediatric formulations with unacceptable properties (palatability, pill size, pill burden, high dosing frequency), high toxicity profiles and low genetic barrier to resistance. Dolutegravir (DTG) is a novel second-generation integrase strand transfer inhibitor (INSTI) that is highly efficacious, safer and easy to use with a higher genetic barrier to the emergence of HIV drug resistance. While DTG is a potential game changer for children, the accelerated rollout of DTG-based ART in children in low- and middle- income countries (LMICs) occurred in the setting of limited research data in pediatric populations. Currently, fundamental questions remain about the adequacy of the weight-band dosing of the generic pediatric DTG scored 10 mg dispersible tablet, drug-drug interactions in the setting of treatment of latent and active tuberculosis (TB) and overall long-term effectiveness of DTG-based ART in children. The goal of this application is to address fundamental research gaps to assure optimal use of DTG in children and adolescents in real-world settings. The following specific aims will be pursued: 1) To evaluate the pharmacokinetics and safety of the generic pediatric dolutegravir 10 mg scored tablet in children with HIV with and with TB coinfection weighing less than 20 kg. 2) To examine the pharmacokinetics and safety of weekly isoniazid and rifapentine (3HP) in children and adolescents and the drug-drug interactions with dolutegravir in those with HIV infection. 3) To examine the longitudinal adherence, virologic response and changes in body mass index, lipid, glycemia, renal, and hepatic profiles in children and adolescents with HIV on DTG-based ART. The results of this project would provide real-world data that has the potential to impact HIV treatment guidelines for children and adolescents.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT Spinal cord injury (SCI) interrupts blood flow, and the O2 partial pressure (PO2) in the injured spinal cord drops to near zero. This contributes to necrosis and secondary injury. Our central hypothesis is that increasing O2 delivery to the injured cervical spinal cord will attenuate inflammation and neuronal cell loss, thereby preserving breathing function. The vast majority of prior O2 therapy studies after SCI use hyperbaric O2 (HBO), and there is considerable support that HBO reduces inflammation and secondary neurodegeneration. However, HBO consists of 100% O2 (hyperoxia), which is easy to implement, but at elevated pressure (hyperbaria), which is challenging to implement. Preliminary data indicate that the more challenging hyperbaria may not be needed. Specifically, normobaric hyperoxia (i.e., 100% O2 at ambient pressure) rapidly restores spinal PO2 after acute SCI and triggers anti-inflammatory mechanisms with a specific impact on microglia. Neuroinflammation after SCI contributes to scarring and neuronal loss, and impairs plasticity in spinal respiratory motor pathways. Thus, Aim 1 will determine if normobaric O2 therapy, initiated acutely (i.e., hours-days) after cervical SCI (cSCI), increases spinal PO2, mitigates microglial-driven spinal neuroinflammation, and preserves breathing ability. Preliminary data also indicate that a 1-hour per day treatment with normobaric hyperoxia has only a modest impact on secondary neuronal loss after SCI (i.e., neuroprotection). However, more robust neuroprotection can be achieved with HBO therapy. Since the fundamental difference between normo- and hyperbaric therapy is total blood O2, we predict that supplementing O2 delivery through alternate means will enable normobaric therapy to achieve greater neuroprotection. To test this idea, we will study perfluorocarbons - molecules that increase plasma O2 solubility and delivery to the injured spinal cord. Preliminary data show that treatment with a “next generation” perfluorocarbon known as NanO2 is safe, well tolerated, and preserves spinal tissues post-SCI. In Aim 2 we will test the hypothesis that combining normobaric hyperoxia with NanO2 acutely after cervical SCI synergistically increases spinal PO2, and promotes neuroprotection in primary (acute) and secondary cSCI. The proposed work will utilize our established cervical SCI models in the rat, including mid-cervical contusion and C2 hemilesion. Outcome measures include 1) cell-specific molecular responses (e.g., neurons, astrocytes and microglia) via flow cytometry, 2) spinal immunohistochemistry and histological neuron counts, 3) in vivo magnetic resonance imaging (MRI) for visualizing lesion volume, and ex vivo MRI for evaluating neural tracts in high- resolution (tractography), 4) respiratory outcomes including diaphragm EMG and breathing in unanesthetized rats, and direct measure of phrenic nerve output in anesthetized rats, and 5) spinal O2 measurements (intraspinal optode).

NIH Research Projects · FY 2025 · 2024-08

In response to RFA-NS-24-015, we propose the University of Florida Partnerships Across Interdisciplinary Networks: Training through Engineering, epidemiology & Addiction Medicine (UF PAIN TEAM) - a T90/R90 interdisciplinary postdoctoral program designed to train the next generation of independent clinical pain researchers. Our UF PAIN TEAM training program is built on the unique strengths of our existing CTSI-developed team training model, adding a pain science emphasis to clinical pain research training and integrating traditional and non-traditional areas of pain research. We will also emphasize collaboration across the research continuum, and further expand the scope of interdisciplinary clinical pain research to include disciplines not traditionally represented in pain research. We will create a unique cohort experience by embedding authentic, collaborative and interdisciplinary experiences in team science into each trainee’s research, preparing them to conduct team science clinical pain research locally and across the broader HEAL PAIN Cohort Program. The proposed team training approach for clinical pain research combines authentic didactic and experiential training in team science; interdisciplinary mentoring that crosses academic barriers; scientific training across the entire biopsychosocial model of pain; and career development mentoring that crosses academic disciplines. We will provide extensive breadth and depth in training to i) 4 post-doctoral scientists (T90; PhD, Dual degree-PhD), and ii) 1 non-citizen, non-permanent resident clinically-trained PhD holder (R90), that are seeking advanced post-doctoral training in scientific areas designated as high priority by HEAL. These areas are nonpharmacological interventions for pain, non-opioid pharmacological treatments for pain, as well as effective interventions for pain and co-morbidities. To accomplish our goals, expert program faculty will provide extensive team training experiences. Postdoctoral trainees will receive an initial commitment for appointments of two years, where 2 teams of trainees will work together and develop their own collaborative projects involving disciplines traditionally and not traditionally represented in clinical pain research. By implementing an integrated team training program, including didactic, research, and professional development activities, we will equip trainees with new skills, knowledge, and expertise to apply these skills in collaborative teams. We will also create a culture of responsible research conduct and professional excellence to ensure that trainees aspire to the high standards of scientific integrity and quality, which will set the tone for their future careers in clinical pain research. Finally, we will disseminate elements of our team training model regionally and nationally via interdisciplinary communities of traditional and non-traditional pain disciplines including the Annual HEAL PURPOSE Network. Collectively, these programs will produce highly skilled, collaborative, and interactive scientists who can engage in team science to generate new knowledge and translate discoveries to tangible advances in the prevention, treatment, and cure of chronic pain.*

NSF Awards · FY 2024 · 2024-08

Almost all organisms have separate male and female sexes, but this common dynamic holds a contradiction: males and females are physiologically different from each other, sometimes extremely so, but share almost the same set of genes. How do differences between the sexes arise from a shared set of genetic starting points and why do various species show wildly different degrees of sex differences? This research seeks to answer these fundamental questions using a remarkable group of insects, the bagworm moths. Both male and female bagworms start life as caterpillars that carry around a protective silk bag as they feed and grow. Males always mature into winged adult moths, but females can develop very differently depending on the species. In some species, females mature into winged moths whereas in others, females either never develop wings and remain much like a larva and crawl to find a mate, or do not develop legs or eyes and never leave their bag. By studying this diverse group of insects and their relationships, researchers will gain a better understanding of how sex differences are genetically controlled and why they vary across the tree of life. This project will provide training in systematics for two postdoctoral researchers and three graduate students. The researchers will collect bagworm species from around the globe to first construct a phylogeny of bagworms to determine how many independent transitions of sexual dimorphism have occurred. They will also test how larval bag structure, local climate, and other variables influence the evolution of sexual dimorphism. Researchers will generate genomic resources for key species exhibiting different levels of dimorphism to uncover the genetic mechanisms that control sexually dimorphic development in each case. This work will uncover the molecular underpinnings of novelty, discerning whether bagworms employ a common molecular framework or have evolved convergent strategies to achieve similar levels of dimorphism. Finally, researchers will assess the role of adaptation in the evolution of sexual dimorphism by testing for positive selection in genes expressed in males only or females only, especially on the sex chromosomes, which harbor many sex-biased genes. This work will help resolve outstanding questions of evolutionary genetics on the role that sex chromosomes play in adaptation across the genome. Results of this work will be disseminated to the broader community through a number of avenues including peer-reviewed publications, conference talks, a museum exhibit at the Florida Museum of Natural History, and a web comic distributed on social media. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

In response to RFA-NS-24-015, we propose the University of Florida Partnerships Across Interdisciplinary Networks: Training through Engineering, epidemiology & Addiction Medicine (UF PAIN TEAM) - a T90/R90 interdisciplinary postdoctoral program designed to train the next generation of independent clinical pain researchers. Our UF PAIN TEAM training program is built on the unique strengths of our existing CTSI-developed team training model, adding a pain science emphasis to clinical pain research training and integrating traditional and non-traditional areas of pain research. We will also emphasize collaboration across the research continuum and further expand the scope of interdisciplinary clinical pain research to include disciplines not traditionally represented in pain research. We will create a unique cohort experience by embedding authentic, collaborative and interdisciplinary experiences in team science into each trainee’s research, preparing them to conduct team science clinical pain research locally and across the broader HEAL PAIN Cohort Program. The proposed team training approach for clinical pain research combines authentic didactic and experiential training in team science; interdisciplinary mentoring that crosses academic barriers; scientific training across the entire biopsychosocial model of pain; and career development mentoring that crosses academic disciplines. We will provide extensive breadth and depth in training to i) 4 post-doctoral scientists (T90; PhD, Dual degree-PhD), and ii) 1 non-citizen, non-permanent resident clinically-trained PhD holder (R90), that are seeking advanced post-doctoral training in scientific areas designated as high priority by HEAL. These areas are nonpharmacological interventions for pain, non-opioid pharmacological treatments for pain, as well as effective interventions for pain and co-morbidities. To accomplish our goals, expert program faculty will provide extensive team training experiences. Postdoctoral trainees will receive an initial commitment for appointments of two years, where 2 teams of trainees will work together and develop their own collaborative projects involving disciplines traditionally and not traditionally represented in clinical pain research. By implementing an integrated team training program, including didactic, research, and professional development activities, we will equip trainees with new skills, knowledge, and expertise to apply these skills in collaborative teams. We will also create a culture of responsible research conduct and professional excellence to ensure that trainees aspire to the high standards of scientific integrity and quality, which will set the tone for their future careers in clinical pain research. Finally, we will disseminate elements of our team training model regionally and nationally via interdisciplinary communities of traditional and non-traditional pain disciplines including the Annual HEAL PURPOSE Network. Collectively, these programs will produce highly skilled, collaborative, and interactive scientists who can engage in team science to generate new knowledge and translate discoveries to tangible advances in the prevention, treatment, and cure of chronic pain.*

NSF Awards · FY 2024 · 2024-08

The origin of neurons is one of the major transitions in Life history, shaping our planet, and relevant to all biomedical implications. Did Nature use one or many ways to make a neuron, elementary circuits, and behaviors? This interdisciplinary study on enigmatic marine animals, ctenophores, will uncover new classes of signal molecules as fundamental integrators of behaviors, and factors that drive the emergence of neurons. Through broad comparisons of neural machinery with state-of-the-art technologies, the project addresses a major problem in neuroscience: when and how neural circuits evolved. Ctenophores also show remarkable neural regeneration capabilities with behavioral recovery. As such, the understanding of the neural mechanisms in these organisms will open new horizons for synthetic biomedicine of the future. This program will also provide integration of educational activity in neuroscience with broad field-type biodiversity research using marine vessel experience directly in the field. The training will combine genomic, computational, developmental, and neurobiological concepts, expanding our understanding of the origins of biological complexity. The worldwide research locations will secure students from underrepresented groups, who will be recruited by apprenticeship programs to trace the emergence of complex traits and speciation. This research is centered on whether neurons evolved from a single ancestral cell lineage, or did neural circuits and, eventually, brains they form develop from different non-homologous lineages as a result of independent neurogenic and migration events? Emerging data suggest that neurons evolved more than twice, and Ctenophora is the sister group to all other animals. This integrative project will use different lineages of ctenophores to investigate early neuronal phylogeny. To test alternative hypotheses of neural system evolution, the team will take advantage of neuron-specific single-cell multi-omics, connectivity, and functional approaches to identify novel transmitters and neural specification molecules and, ultimately, reconstruct ancestral toolkits and cell populations composing ctenophore neural nets. In doing so, this study will re-evaluate the criteria of how homology can be assigned in chimeric neural populations across Metazoa. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.